
import pandas as pd
from bs4 import BeautifulSoup
import requests
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import date
from datetime import timedelta
import re
import io


url = "https://raw.githubusercontent.com/yanakoyo/da_job_market_for_data_analysts/main/masterskaya_yandex_2022_09_07.csv"

download = requests.get(url).content

df = pd.read_csv(io.StringIO(download.decode('utf-8')), usecols=['html'])


df.head()


df['html'][55][:500]

'\n  <div>\n    <div class="\n        jobs-details__main-content jobs-details__main-content--single-pane full-width\n        ">\n        \n<!---->\n      \n<!---->\n      <div>\n        <div class="jobs-unified-top-card t-14">\n  <div class="relative">\n\n    <div class="jobs-unified-top-card__content--two-pane">\n<!---->\n        <a href="/jobs/view/3255876665/?alternateChannel=search&amp;refId=yH2C6IIsgieiiXwBpe7xWg%3D%3D&amp;trackingId=jsZrxd%2BGW1A2E4cm1Y3Qpw%3D%3D&amp;trk=d_flagship3_search_srp_jobs" id="e'


df['title'] = df['html'].apply(lambda x:  BeautifulSoup(x).find('h2').text.strip())


df.sample(12, random_state=188)


print('Total jobs in current dataset:', df.shape[0])

Total jobs in current dataset: 772


df['title'].unique()[:15]

array(['Stage - Assistant Ingénieur Qualité - Beyrand (H/F)',
       'développeur matlab/simulink, secteur automobile f/h',
       'Online Data Analyst', 'Online Data Analyst - Belgium',
       'Data Analyst', 'Alternant/ Alternante data analyst M/F',
       'Alternant(e) Data Scientist (H/F)',
       'ALTERNANCE- TOTALENERGIES RENEWABLES - Data Engineer (H/F)',
       'Data Engineer (with Python)', 'Junior Test Analyst',
       'Data Analyst H/F',
       'Data Scientist e-Commerce & Acquisition Monétique',
       'Ingénieur de recherche en traitement de données de mobilité H/F',
       'INGENIERO/A PRODUCTO Y CALIDAD AUTOMOCIÓN',
       'Data Scientist - Experience'], dtype=object)


df['title'] = df['title'].str.upper()


analyst_titles = ['DATA ANALYST', 'DATA SCIENTIST', 'TEST ANALYST', 'DATA JUNIOR ANALYST']


df = df.loc[df['title'].str.contains('|'.join(analyst_titles))]


df['title'].unique()[:20]

array(['ONLINE DATA ANALYST', 'ONLINE DATA ANALYST - BELGIUM',
       'DATA ANALYST', 'ALTERNANT/ ALTERNANTE DATA ANALYST M/F',
       'ALTERNANT(E) DATA SCIENTIST (H/F)', 'JUNIOR TEST ANALYST',
       'DATA ANALYST H/F',
       'DATA SCIENTIST E-COMMERCE & ACQUISITION MONÉTIQUE',
       'DATA SCIENTIST - EXPERIENCE', 'STAGE - DATA ANALYST (H/F)',
       'DATA ANALYST (F/H)', 'DATA ANALYST (TABLEAU)',
       '(JUNIOR) DATA SCIENTIST (M/W/D)', 'JUNIOR DATA SCIENTIST',
       'DATA SCIENTIST EN STAGE H/F', 'DATA SCIENTIST',
       'ONLINE DATA ANALYST | FRENCH SPEAKER',
       'ONLINE DATA ANALYST | FLEXIBLE WORK',
       'ONLINE DATA ANALYST | REMOTE OPPORTUNITY', 'DATA ANALYST (IT)'],
      dtype=object)


print(df.shape[0], 'relevant jobs')

303 relevant jobs


df['link'] = df['html'].apply(lambda x: "https://linkedin.com" + BeautifulSoup(x).find('a').get('href'))


df['link'].head()

2    https://linkedin.com/jobs/view/3248499929/?alt...
3    https://linkedin.com/jobs/view/3248879065/?alt...
4    https://linkedin.com/jobs/view/3257222954/?alt...
5    https://linkedin.com/jobs/view/3257228331/?alt...
6    https://linkedin.com/jobs/view/3244114599/?alt...
Name: link, dtype: object


def get_geo(cell):
    try:
        return BeautifulSoup(cell).find('span', class_ = 'jobs-unified-top-card__bullet').text.strip()
    except:
        return np.nan


df['geo'] = df['html'].apply(get_geo)


df['geo'].head(10)

2           Skara, Vastra Gotaland County, Sweden
3          West Flanders, Flemish Region, Belgium
4             Mecklenburg-West Pomerania, Germany
5                                Hamburg, Germany
6     Rousset, Provence-Alpes-Côte d'Azur, France
7                    Massy, Île-de-France, France
10                              Budapest, Hungary
11                     Przemyśl Metropolitan Area
12                    Toulouse, Occitanie, France
13                          Île-de-France, France
Name: geo, dtype: object


def get_city(cell):
    if len(cell.split(',')) > 1:
        return cell.split(',')[0].strip()
    elif 'Metropolitan' in cell or 'Greater' in cell or 'Region' in cell:
        return cell.replace(
            'Greater', '').replace(
            'Metropolitan', '').replace(
            'Area', '').replace(
            'Region','').strip()
    else:
        return np.nan


df['city'] = df['geo'].apply(get_city)


df['city'].sample(10)

330                 Berlin
450               Hannover
64                   Paris
495       Fenchurch Street
561             St-Étienne
407                Utrecht
406             Södertälje
569                Fareham
87     Cologne Bonn Region
378               Toulouse
Name: city, dtype: object


def get_country(cell):
    if 'Italy Metropolitan Area' in cell:
        return cell.replace('Italy Metropolitan Area', 'Italy').split(',')[-1].strip()
    if len(cell.split(',')) > 1:
        return cell.split(',')[-1].strip()
    elif "Metropolitan" in cell or "Greater" in cell or "Region" in cell:
        return np.nan
    else:
        return cell


df['country'] = df['geo'].apply(get_country)


df['country'].value_counts()

United Kingdom    67
France            53
Germany           46
Poland            15
Italy             13
Switzerland       11
Netherlands       11
Sweden            11
Ireland            7
Spain              7
Belgium            6
Finland            5
Slovakia           5
Denmark            5
Norway             4
Portugal           4
Slovenia           4
Bulgaria           3
Croatia            3
Hungary            2
Romania            2
Czechia            1
Luxembourg         1
Gibraltar          1
Name: country, dtype: int64


df[df['country'] == 'Germany']['city'].unique()

array(['Mecklenburg-West Pomerania', 'Hamburg', 'Bad Wimpfen',
       'North Rhine-Westphalia', 'Berlin', 'Stuttgart', 'Frankfurt',
       'Munich', 'Dresden', 'Karlsruhe', 'Cologne Bonn Region', nan,
       'Offenbach', 'Bonn', 'Böblingen', 'Mainz', 'Düsseldorf',
       'Hannover', 'Augsburg', 'Cologne', 'Frankfurt am Main'],
      dtype=object)


df[df['country'].isna()]['city'].to_list()

['Przemyśl',
 'Wroclaw',
 'Grudziadz',
 'Norrköping',
 'Radom',
 'Zamosc',
 'Edinburgh',
 'Berlin',
 'Lodz',
 'Lodz',
 'Brussels',
 'Milan',
 'Zurich',
 'Barcelona',
 'Barcelona',
 'Ghent']


#df.loc[df['city'] == 'Hannover-Braunschweig-Göttingen-Wolfsburg', 'city'] = 'Hannover'
#df.loc[df['city'] == 'Cologne Bonn', 'city'] = 'Cologne'


poland = ['Wroclaw', 'Gdansk', 'Przemyśl', 'Grudziadz', 'Radom', 'Zamosc', 'Lodz', 'Warsaw']
france = ['Paris', 'Nantes']
italy = ['Turin', 'Rome', 'Milan', 'Bologna', 'Modena', 'Genoa']
sweden = ['Norrköping']
uk = ['Edinburgh']
germany = ['Berlin', 'Cologne', 'Hannover']
spain = ['Cáceres', 'Gijón', 'Barcelona', 'Valencia']
belgium = ['Brussels', 'Mons', 'Ghent', 'Bruges', 'Kortrijk', 'Namur', 'Antwerp', 'Liege', 'Louvain']
romania = ['Bucharest',]
switzerland = ['Zurich',]
greece = ['Athens',]

nan_countries = df.loc[df['country'].isna()].index.to_list()


def country_man(row):
    '''
    Function checks if the city in the cell 
    is in the list given,
    and returns the name of the country accordingly
    '''
    
    city = row['city']
    country = row['country']

    if city in poland:
        return 'Poland'
    elif city in france:
        return 'France'
    elif city in italy:
        return 'Italy'
    elif city in sweden:
        return 'Sweden'
    elif city in uk:
        return 'UK'
    elif city in germany:
        return 'Germany'
    elif city in spain:
        return 'Spain'
    elif city in belgium:
        return 'Belgium'
    elif city in romania:
        return 'Romania'
    elif city in switzerland:
        return 'Switzerland'
    elif city in greece:
        return 'Greece'
    else:
        return country


df['country_1'] = df[df['city'].index.isin(nan_countries)].apply(country_man, axis=1)


df.head()


df['country'] = df['country'].combine_first(df['country_1'])


df.drop(columns=['country_1','geo'], inplace=True)


df.sample(5)


def get_candidats(cell):
    try:
        return int(
            BeautifulSoup(cell).find(
            'span', class_ = 'jobs-unified-top-card__applicant-count').text.strip().split(' ')[0]
        )      
    except:
        return np.nan


df['candidats'] = df['html'].apply(get_candidats)


df.sample(7)


print(df['candidats'].describe())

plt.figure(figsize=(14, 8))

sns.histplot(data=df, x='candidats', color='red', label='jobs', bins=50)

plt.title('Number of applicants per job', loc="center", fontsize=15)
plt.ylabel('Vacancies', fontsize=11)
plt.xlabel('Number of applicants', fontsize=11);

count    269.000000
mean      33.498141
std       38.871067
min        1.000000
25%        7.000000
50%       17.000000
75%       44.000000
max      192.000000
Name: candidats, dtype: float64


def get_company(cell):
    try:
        return BeautifulSoup(cell).find('span', class_ = 'jobs-unified-top-card__company-name').text.strip()
    except:
        return np.nan


df['company'] = df['html'].apply(get_company)


df.head()


df['company'].value_counts().head(5)

TELUS International AI Data Solutions    32
TELUS International                      27
Positive Thinking Company                 6
TUI                                       4
Appen                                     4
Name: company, dtype: int64


def get_insight(cell):
    try:
        return BeautifulSoup(cell).find(
            'li', class_ = 'jobs-unified-top-card__job-insight').text.strip()
    except:
        return np.nan


df['insight'] = df['html'].apply(get_insight)


df['insight'].value_counts()

Full-time · Entry level                              206
Part-time · Entry level                               51
Contract · Entry level                                27
Full-time                                              7
Temporary · Entry level                                4
Internship · Entry level                               2
£19,000/yr - £28,000/yr · Full-time                    1
Entry level                                            1
Part-time                                              1
£30,000/yr - £35,000/yr · Full-time · Entry level      1
£25,000/yr - £27,500/yr · Contract · Entry level       1
Name: insight, dtype: int64


def get_time(cell):
    if len(str(cell).split('·')) > 1:
        if '£19,000/yr - £28,000/yr' in cell:
            return cell.split('·')[-1].strip()
        else:
            return cell.split('·')[-2].strip()
    else:
        return np.nan


df['time'] = df['insight'].apply(get_time)


df['time'].value_counts()

Full-time     208
Part-time      51
Contract       28
Temporary       4
Internship      2
Name: time, dtype: int64


def get_w_type(cell):
    try:
        return BeautifulSoup(cell).find(
            'span', class_ = 'jobs-unified-top-card__workplace-type').text.strip()
    except:
        return np.nan


df['w_type'] = df['html'].apply(get_w_type)


df['w_type'].value_counts()

On-site    132
Remote      80
Hybrid      32
Name: w_type, dtype: int64


def get_field_and_size(cell):
    try:
        return BeautifulSoup(cell).find(
            'div', class_ = 'mt5 mb2').find_all('li')[1].text.strip()
    except:
        return np.nan


df['field_size'] = df['html'].apply(get_field_and_size)


df['field_size'].unique()[:10]

array(['10,001+ employees · IT Services and IT Consulting',
       '10,001+ employees · Semiconductor Manufacturing',
       '10,001+ employees · Retail',
       '10,001+ employees · Motor Vehicle Manufacturing',
       '201-500 employees · Human Resources Services',
       '10,001+ employees · Banking',
       '5,001-10,000 employees · Musicians',
       '10,001+ employees · Industrial Machinery Manufacturing',
       '1,001-5,000 employees · IT Services and IT Consulting',
       '51-200 employees · Staffing and Recruiting'], dtype=object)


def get_size(cell):
    return str(cell).split('·')[0].strip()


df['c_size'] = df['field_size'].apply(get_size)


def company_size(row):
    '''
    Function returns generalized size
    of the company, based on amount of employees
    '''  
    size = row['c_size']
    
    if ('1,00' in size) or ('10,00' in size):
    #elif ('501-1,000' in size) or ('1,001-5,000' in size) or ('5,001-10,000' in size) or ('10,001+' in size):
        return 'big (500+)'
    elif ('20' in size):
        return 'middle (50-500)'
    elif ('1-10' in size) or ('11-50' in size):
        return 'small (1-50)'
    else:
        return 'other'


df['company_size'] = df[['c_size']].apply(company_size, axis=1)


df['company_size'].value_counts()

big (500+)         212
middle (50-500)     49
small (1-50)        30
other               12
Name: company_size, dtype: int64


def get_field(cell):
    return str(cell).split('·')[-1].strip()


df['field'] = df['field_size'].apply(get_field)


df['field'].unique()[:30]

array(['IT Services and IT Consulting', 'Semiconductor Manufacturing',
       'Retail', 'Motor Vehicle Manufacturing',
       'Human Resources Services', 'Banking', 'Musicians',
       'Industrial Machinery Manufacturing', 'Staffing and Recruiting',
       'Financial Services', 'Insurance', 'Food and Beverage Services',
       'Pharmaceutical Manufacturing',
       'Aviation and Aerospace Component Manufacturing',
       'Automation Machinery Manufacturing',
       'Business Consulting and Services', 'Oil and Gas',
       'Broadcast Media Production and Distribution', 'Civil Engineering',
       'Appliances, Electrical, and Electronics Manufacturing',
       'Software Development', 'Retail Luxury Goods and Jewelry',
       'Internet Marketplace Platforms', 'Utilities',
       'Advertising Services', 'Technology, Information and Internet',
       'Consumer Services', 'Artists and Writers', 'Telecommunications',
       'Hospitality'], dtype=object)


no_list_field = ['See recent hiring trends', 'employees', 'See how you compare']


df.loc[df['field'].str.contains('|'.join(no_list_field)), 'field'] = np.nan


df.head()


def date_posted(cell):
    try:
        return BeautifulSoup(cell).find(
            'span', class_ = 'jobs-unified-top-card__posted-date').text.strip()
    except:
        return np.nan


df['posted_date'] = df['html'].apply(date_posted)


df['posted_date'].value_counts()

1 day ago         66
6 days ago        64
5 days ago        51
2 days ago        38
8 hours ago       16
4 days ago        11
9 hours ago        8
3 days ago         7
23 hours ago       7
2 hours ago        7
21 hours ago       4
7 hours ago        4
10 hours ago       3
5 hours ago        2
4 hours ago        2
22 hours ago       2
1 week ago         1
16 hours ago       1
37 minutes ago     1
14 hours ago       1
6 minutes ago      1
11 hours ago       1
19 hours ago       1
3 hours ago        1
18 hours ago       1
6 hours ago        1
13 hours ago       1
Name: posted_date, dtype: int64


df['posted_date'] = df['posted_date'].replace({' days ago': '', ' day ago': '', '1 week ago': '7'}, regex=True)


df['posted_date'] = df['posted_date'].str.replace('.* ago', '0', regex=True)


df['posted_date'].value_counts()

1    66
0    65
6    64
5    51
2    38
4    11
3     7
7     1
Name: posted_date, dtype: int64


df['posted_date'] = df['posted_date'].astype(int)


df['posted_date'] = df['posted_date'].apply(lambda x: date(2022, 9, 7) - timedelta(days=x))


df['posted_date'].value_counts()

2022-09-06    66
2022-09-07    65
2022-09-01    64
2022-09-02    51
2022-09-05    38
2022-09-03    11
2022-09-04     7
2022-08-31     1
Name: posted_date, dtype: int64


df[df['posted_date'] == date(2022, 8, 31)]


df.drop(index=(501), axis=0, inplace=True)


def get_description(df):
  soup = BeautifulSoup(df, 'lxml')
  desc_all = soup.find_all('article', class_='jobs-description__container m4')
  for desc in desc_all:
    element = desc.get_text().strip().replace('\n', ' ')
    #element = re.sub('[^a-zA-Z0-9 \n\.]', '', element)
    element = element.replace("\'", " ")
    element = element.replace('  ', ' ')
    return element


df['description'] = df['html'].apply(lambda x: get_description(x))


df.head(5)


skills = (['datahub', 'api', 'github', 'google analytics', 'adobe analytics', 'ibm coremetrics', 'omniture'
            'gitlab', 'erwin', 'hadoop', 'spark', 'hive'
           'databricks', 'aws', 'gcp', 'azure','excel',
            'redshift', 'bigquery', 'snowflake',  'hana'
            'grafana', 'kantar', 'spss', 'a/b testing', 'ab testing',
           'asana', 'basecamp', 'jira', 'dbeaver','trello', 'miro', 'salesforce', 
           'rapidminer', 'thoughtspot',  'power point',  'docker', 'jenkins','integrate.io', 'talend', 'apache nifi','aws glue','pentaho','google data flow',
             'azure data factory','xplenty','skyvia','iri voracity','xtract.io','dataddo', 'ssis',
             'hevo data','informatica','oracle data integrator','k2view','cdata sync','querysurge', 
             'rivery', 'dbconvert', 'alooma', 'stitch', 'fivetran', 'matillion','streamsets','blendo',
             'iri voracity','logstash', 'etleap', 'singer', 'apache camel','actian', 'airflow', 'luidgi', 'datastage',
           'python', 'vba', 'scala', ' r ', 'java script', 'julia', 'sql', 'matlab', 'java', 'html', 'c++', 'sas',
           'data studio', 'tableau', 'looker', 'powerbi', 'cognos', 'microstrategy', 'spotfire',
             'sap business objects','microsoft sql server', 'oracle business intelligence', 'yellowfin',
             'webfocus','sas visual analytics', 'targit', 'izenda',  'sisense', 'statsbot', 'panorama', 'inetsoft',
             'birst', 'domo', 'metabase', 'redash', 'power bi', 'alteryx', 'dataiku', 'qlik sense', 'qlikview'
          ])


def get_skills(cell):
    list_skills = []
    for skill in skills:
        if skill in cell.lower().replace('powerbi', 'power bi'):
            list_skills.append(skill)
    return list_skills


df['skills'] = df.description.apply(get_skills)


df.head()


df.duplicated(subset=['html']).sum()

0


df_vis = df[['title', 'link', 
             'city', 'country',
             'w_type', 'time',
             'company','field', 'company_size',
             'posted_date', 'candidats',
             'description', 'skills']]


df_vis.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 302 entries, 2 to 763
Data columns (total 13 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   title         302 non-null    object 
 1   link          302 non-null    object 
 2   city          283 non-null    object 
 3   country       302 non-null    object 
 4   w_type        243 non-null    object 
 5   time          292 non-null    object 
 6   company       302 non-null    object 
 7   field         284 non-null    object 
 8   company_size  302 non-null    object 
 9   posted_date   302 non-null    object 
 10  candidats     268 non-null    float64
 11  description   302 non-null    object 
 12  skills        302 non-null    object 
dtypes: float64(1), object(12)
memory usage: 33.0+ KB


df_vis[df_vis['city'].isna()]['w_type'].value_counts()

Remote    18
Name: w_type, dtype: int64


df_vis = df_vis.explode('skills')


df_vis.to_csv('linkedin_for_vis.csv', index = False)

	html
0	\n <div>\n <div class="\n jobs-deta...
1	\n <div>\n <div class="\n jobs-deta...
2	\n <div>\n <div class="\n jobs-deta...
3	\n <div>\n <div class="\n jobs-deta...
4	\n <div>\n <div class="\n jobs-deta...

	html	title
405	\n <div>\n <div class="\n jobs-deta...	Data Engineer junior \| MedTech
342	\n <div>\n <div class="\n jobs-deta...	Online Data Analyst
551	\n <div>\n <div class="\n jobs-deta...	Sample Management Data Analyst
386	\n <div>\n <div class="\n jobs-deta...	Internet Analyst \| Greater Turin Metropolitan ...
160	\n <div>\n <div class="\n jobs-deta...	STAGE - DEVELOPPEUR FULLSTACK- Direction IT EX...
182	\n <div>\n <div class="\n jobs-deta...	Data Engineer for Business Intelligence/ Data ...
556	\n <div>\n <div class="\n jobs-deta...	Développeur Python Back-end
607	\n <div>\n <div class="\n jobs-deta...	Interim HR Data Analyst
425	\n <div>\n <div class="\n jobs-deta...	Data Analyst
255	\n <div>\n <div class="\n jobs-deta...	Data Analyst
695	\n <div>\n <div class="\n jobs-deta...	Project Services Assistant
40	\n <div>\n <div class="\n jobs-deta...	Data engineer Big Data (H/F) - ELC

	html	title	link	city	country
357	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST H/F	https://linkedin.com/jobs/view/3250772628/?alt...	Nanterre	France
86	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST IN FORENSIC TECHNOLOGY SERVICES TEAM	https://linkedin.com/jobs/view/3196639905/?alt...	Prague	Czechia
476	\n <div>\n <div class="\n jobs-deta...	PT COMPLIANCE HUB - DATA ANALYST	https://linkedin.com/jobs/view/3243388246/?alt...	Lisbon	Portugal
461	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST - DEMAND	https://linkedin.com/jobs/view/3180730881/?alt...	Paris	France
286	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST - 9 MONTH FIXED TERM CONTRACT	https://linkedin.com/jobs/view/3249619764/?alt...	Bury St. Edmunds	United Kingdom

	html	title	link	city	country	candidats
288	\n <div>\n <div class="\n jobs-deta...	ONLINE DATA ANALYST	https://linkedin.com/jobs/view/3248883741/?alt...	Split	Croatia	17.0
77	\n <div>\n <div class="\n jobs-deta...	ALTERNANCE - DATA ANALYST/DATAVIZ SPECIALIST H/F	https://linkedin.com/jobs/view/3245828255/?alt...	Massy	France	62.0
161	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST	https://linkedin.com/jobs/view/3212194589/?alt...	Sofia	Bulgaria	22.0
146	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST	https://linkedin.com/jobs/view/3256464458/?alt...	Pamplona	Spain	NaN
515	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST (M/F/D) WORKLOAD PLANNING (BUSINE...	https://linkedin.com/jobs/view/3256652921/?alt...	Augsburg	Germany	NaN
47	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST	https://linkedin.com/jobs/view/3254077579/?alt...	Avrillé	France	2.0
116	\n <div>\n <div class="\n jobs-deta...	DATA DATA ANALYST WITH SPANISH	https://linkedin.com/jobs/view/3249230346/?alt...	Gdynia	Poland	2.0

Data Analyst jobs in Europe | September 2022¶

Contents

Introduction¶

Final dashboard preview¶

Extracting the data¶

Preparation¶

Job title¶

Getting the link¶

Location¶

Number of applicants¶

Company name¶

Level and time¶

Type¶

Company size¶

Getting the field¶

Date¶

Description¶

Skills¶

Preparation for visualisation¶

Export of csv file¶

	html	title	link	geo	city	country	country_1
2	\n <div>\n <div class="\n jobs-deta...	ONLINE DATA ANALYST	https://linkedin.com/jobs/view/3248499929/?alt...	Skara, Vastra Gotaland County, Sweden	Skara	Sweden	NaN
3	\n <div>\n <div class="\n jobs-deta...	ONLINE DATA ANALYST - BELGIUM	https://linkedin.com/jobs/view/3248879065/?alt...	West Flanders, Flemish Region, Belgium	West Flanders	Belgium	NaN
4	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST	https://linkedin.com/jobs/view/3257222954/?alt...	Mecklenburg-West Pomerania, Germany	Mecklenburg-West Pomerania	Germany	NaN
5	\n <div>\n <div class="\n jobs-deta...	DATA ANALYST	https://linkedin.com/jobs/view/3257228331/?alt...	Hamburg, Germany	Hamburg	Germany	NaN
6	\n <div>\n <div class="\n jobs-deta...	ALTERNANT/ ALTERNANTE DATA ANALYST M/F	https://linkedin.com/jobs/view/3244114599/?alt...	Rousset, Provence-Alpes-Côte d'Azur, France	Rousset	France	NaN